From spreadsheets to sugar content modeling: A data mining approach

- We derived empirical models for sugar content using field data from a sugarcane mill.
- We show how the data mining framework can be used to model sugar content.
- A feature selection algorithm was able to identify good predictors in the dataset.
- On average, models achieved errors of 2.5% of the lower value in the test set.
- The best models have 90% of error within 5.4 kg Mg−1.

Sugarcane mills need sugar content estimates in advance to establish their commercial strategy. To obtain these estimates, mills rely on historical averages or maturation curves. Crop models have also been developed to provide those estimates. Leveraging mill data about fields and sugar content at harvest, we developed empirical models using different data mining techniques along with the RReliefF algorithm for feature selection. The best model was attained with Random Forest with features selected by RReliefF, having a mean absolute error of 2.02 kg Mg−1. This model outperformed Support Vector Regression and Regression Trees with and without feature selection. Models were also evaluated by the Regression Error Characteristic Curves, which showed that the best model was able to predict 90% of the observations within a precision of 5.40 kg Mg−1.